Phil Mehler

Bio: Phil Mehler is an academic researcher from University of Colorado Denver. The author has contributed to research in topics: Anorexia nervosa (differential diagnoses). The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Reframing anorexia nervosa as a metabo-psychiatric disorder

Abstract: Anorexia nervosa (AN) is a serious and often fatal illness. Despite decades of research, investigators have failed to adequately advance our understanding of the biological aspects of AN that could inform the development of effective interventions. Genome-wide association studies are revealing the important role of metabolic factors in AN, and studies of the gastrointestinal tract are shedding light on disruptions in enteric microbial communities and anomalies in gut morphology. In this opinion piece, we review the state of the science through the lens of the clinical presentation of illness. We project how the integration of rigorous science in genomics and microbiology, in collaboration with experienced clinicians, has the potential to markedly enhance treatment outcome via precision interventions.

Eating disorders and palliative care specialists require definitional consensus and clinical guidance regarding terminal anorexia nervosa: addressing concerns and moving forward

Abstract: Premature deaths are estimated to occur in 5-20% of patients with anorexia nervosa (AN). Among them, some patients with severe and enduring anorexia nervosa (SE-AN) will die due to the medical complications of malnutrition or to suicide. Almost no literature provides guidance to patients, clinicians, and loved ones regarding clinical characteristics of those with SE-AN who recognize and accept the fact that they will not be able to survive their disease. Consistent with general medical literature on terminal illness and based on the authors' work with patients at this phase of life, we previously described four clinical characteristics of the small group of SE-AN patients who may be considered to have a terminal eating disorder. Following publication of this article, several opinions objecting to these formulations were published. The goals of this article are to respond to the key themes of concern posed by these objections, to extend our discussion of the palliative care and associated needs of these patients and their families, and to suggest ways in which the eating disorder and palliative care fields might develop more definitive criteria and consensus guidelines for the assessment and management of these patients.Based on a selective narrative review of the literature, our combined experiences with these patients, and clinical reasoning, we address critiques grouped around five major themes: that (1) labels such as terminal AN are dangerous; (2) since AN is a treatable disorder, no SE-AN patients should be considered terminal; (3) a terminal psychiatric condition cannot be defined; (4) the proposed definition is not specific enough; and (5) considerations regarding mental capacity in the proposed criteria do not sufficiently account for the psycho-cognitive impairments in AN.Our analysis responds to the critiques of our original proposed clinical characteristics of those with terminal AN. While refuting many of these critiques, we also appreciate the opportunity to refine our discussion of this complex topic and identify that there are multiple stages of SE-AN that can result in good clinical outcomes. Only when all of these have failed to provide adequate amelioration of suffering do a low number of patients progress to terminal AN.By further refining our discussion of terminal AN, we aim to encourage eating disorders and palliative care specialists to develop expert consensus definitions for terminal AN and to generate authoritative clinical guidance for management of this population. By validating terminal AN as a distinct condition, patients with this subcategory of SE-AN, their families, and their caregivers facing end-of-life concerns may be better able to access palliative and hospice care and related services to help improve their overall experiences at this phase of life.

Are the Effects of Malnutrition on the Gut Microbiota–Brain Axis the Core Pathologies of Anorexia Nervosa?

Abstract: Anorexia nervosa (AN) is a disabling, costly, and potentially deadly illness. Treatment failure and relapse after treatment are common. Several studies have indicated the involvement of the gut microbiota–brain (GMB) axis. This narrative review hypothesizes that AN is driven by malnutrition-induced alterations in the GMB axis in susceptible individuals. According to this hypothesis, initial weight loss can voluntarily occur through dieting or be caused by somatic or psychiatric diseases. Malnutrition-induced alterations in gut microbiota may increase the sensitivity to anxiety-inducing gastrointestinal hormones released during meals, one of which is cholecystokinin (CCK). The experimental injection of a high dose of its CCK-4 fragment in healthy individuals induces panic attacks, probably via the stimulation of CCK receptors in the brain. Such meal-related anxiety attacks may take part in developing the clinical picture of AN. Malnutrition may also cause increased effects from appetite-reducing hormones that also seem to have roles in AN development and maintenance. The scientific background, including clinical, microbiological, and biochemical factors, of AN is discussed. A novel model for AN development and maintenance in accordance with this hypothesis is presented. Suggestions for future research are also provided.

The impact of anorexia nervosa and obesity polygenic risk on childhood growth: a 20-year longitudinal population-based study

Abstract: Background Deviating growth from the norm during childhood has been associated with anorexia nervosa (AN) and obesity later in life. In this study, we examined whether polygenic scores (PGS) for AN and obesity are associated, individually or combined, with a range of anthropometric trajectories spanning the first two decades of life. Methods AN-PGS and obesity-PGS were calculated for participants of the Avon Longitudinal Study of Parents and Children (ALSPAC; N= 8,654 participants with genotype data and at least one outcome measure). Using generalized (mixed) linear models, we associated PGS with trajectories of weight, height, body mass index (BMI), fat mass index (FMI), lean mass index (LMI), and bone mineral density (BMD). Growth trajectories were derived using spline modeling or mixed effects modeling. Results Between age 5-24 years, Females with one SD higher AN-PGS had on average a 0.01% lower BMI trajectory, and between age 10-24 years a 0.01% lower FMI trajectory and 0.05% lower weight trajectory. Higher obesity-PGS was associated with higher BMI, FMI, LMI, BMD, weight, and lower height trajectories in both sexes. The average growth trajectories of females with high AN-PGS/low obesity-PGS remained consistently lower than those with low AN-PGS/low obesity-PGS; this difference did not reach statistical significance. However, post-hoc comparisons suggest that females with high AN-PGS/low obesity-PGS did follow lower growth trajectories compared to those with high PGS for both traits. Conclusion AN-PGS and obesity-PGS have detectable sex-dependent effects on a range of anthropometry trajectories. These findings encourage further research in understanding how the AN-PGS and the obesity-PGS co-influence growth during childhood in which the obesity-PGS can mitigate the effects of the AN-PGS.

Eating disorders and palliative care specialists require definitional consensus and clinical guidance regarding terminal anorexia nervosa: addressing concerns and moving forward

Abstract: Premature deaths are estimated to occur in 5-20% of patients with anorexia nervosa (AN). Among them, some patients with severe and enduring anorexia nervosa (SE-AN) will die due to the medical complications of malnutrition or to suicide. Almost no literature provides guidance to patients, clinicians, and loved ones regarding clinical characteristics of those with SE-AN who recognize and accept the fact that they will not be able to survive their disease. Consistent with general medical literature on terminal illness and based on the authors' work with patients at this phase of life, we previously described four clinical characteristics of the small group of SE-AN patients who may be considered to have a terminal eating disorder. Following publication of this article, several opinions objecting to these formulations were published. The goals of this article are to respond to the key themes of concern posed by these objections, to extend our discussion of the palliative care and associated needs of these patients and their families, and to suggest ways in which the eating disorder and palliative care fields might develop more definitive criteria and consensus guidelines for the assessment and management of these patients.Based on a selective narrative review of the literature, our combined experiences with these patients, and clinical reasoning, we address critiques grouped around five major themes: that (1) labels such as terminal AN are dangerous; (2) since AN is a treatable disorder, no SE-AN patients should be considered terminal; (3) a terminal psychiatric condition cannot be defined; (4) the proposed definition is not specific enough; and (5) considerations regarding mental capacity in the proposed criteria do not sufficiently account for the psycho-cognitive impairments in AN.Our analysis responds to the critiques of our original proposed clinical characteristics of those with terminal AN. While refuting many of these critiques, we also appreciate the opportunity to refine our discussion of this complex topic and identify that there are multiple stages of SE-AN that can result in good clinical outcomes. Only when all of these have failed to provide adequate amelioration of suffering do a low number of patients progress to terminal AN.By further refining our discussion of terminal AN, we aim to encourage eating disorders and palliative care specialists to develop expert consensus definitions for terminal AN and to generate authoritative clinical guidance for management of this population. By validating terminal AN as a distinct condition, patients with this subcategory of SE-AN, their families, and their caregivers facing end-of-life concerns may be better able to access palliative and hospice care and related services to help improve their overall experiences at this phase of life.

The impact of anorexia nervosa and BMI polygenic risk on childhood growth: A 20-year longitudinal population-based study

Abstract: Growth deviating from the norm during childhood has been associated with anorexia nervosa (AN) and obesity later in life. In this study, we examined whether polygenic scores (PGSs) for AN and BMI are associated with growth trajectories spanning the first two decades of life. AN PGSs and BMI PGSs were calculated for participants of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 8,654). Using generalized (mixed) linear models, we associated PGSs with trajectories of weight, height, body mass index (BMI), fat mass index (FMI), lean mass index (LMI), and bone mineral density (BMD). Female participants with AN PGSs one standard deviation (SD) higher had, on average, 0.004% slower growth in BMI between the ages 6.5 and 24 years and a 0.4% slower gain in BMD between the ages 10 and 24 years. Higher BMI PGSs were associated with faster growth for BMI, FMI, LMI, BMD, and weight trajectories in both sexes throughout childhood. Female participants with both a high AN PGS and a low BMI PGS showed slower growth compared to those with both a low AN PGS and a low BMI PGS. We conclude that AN PGSs and BMI PGSs have detectable sex-specific effects on growth trajectories. Female participants with a high AN PGS and low BMI PGS likely constitute a high-risk group for AN, as their growth was slower compared to their peers with high PGSs on both traits. Further research is needed to better understand how the AN PGS and the BMI PGS co-influence growth during childhood and whether a high BMI PGS can mitigate the effects of a high AN PGS. Growth deviating from the norm during childhood has been associated with anorexia nervosa (AN) and obesity later in life. In this study, we examined whether polygenic scores (PGSs) for AN and BMI are associated with growth trajectories spanning the first two decades of life. AN PGSs and BMI PGSs were calculated for participants of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 8,654). Using generalized (mixed) linear models, we associated PGSs with trajectories of weight, height, body mass index (BMI), fat mass index (FMI), lean mass index (LMI), and bone mineral density (BMD). Female participants with AN PGSs one standard deviation (SD) higher had, on average, 0.004% slower growth in BMI between the ages 6.5 and 24 years and a 0.4% slower gain in BMD between the ages 10 and 24 years. Higher BMI PGSs were associated with faster growth for BMI, FMI, LMI, BMD, and weight trajectories in both sexes throughout childhood. Female participants with both a high AN PGS and a low BMI PGS showed slower growth compared to those with both a low AN PGS and a low BMI PGS. We conclude that AN PGSs and BMI PGSs have detectable sex-specific effects on growth trajectories. Female participants with a high AN PGS and low BMI PGS likely constitute a high-risk group for AN, as their growth was slower compared to their peers with high PGSs on both traits. Further research is needed to better understand how the AN PGS and the BMI PGS co-influence growth during childhood and whether a high BMI PGS can mitigate the effects of a high AN PGS. Anorexia nervosa (AN) is a serious psychiatric disorder that is characterized by low fat and lean mass.1Treasure J. Zipfel S. Micali N. Wade T. Stice E. Claudino A. Schmidt U. Frank G.K. Bulik C.M. Wentz E. Anorexia nervosa.Nat. Rev. Dis. Prim. 2015; 1: 15074-15122https://doi.org/10.1038/nrdp.2015.74Crossref PubMed Scopus (161) Google Scholar, 2Polito A. Cuzzolaro M. Raguzzini A. Censi L. Ferro-Luzzi A. Body composition changes in anorexia nervosa.Eur. J. Clin. Nutr. 1998; 52: 655-662https://doi.org/10.1038/sj.ejcn.1600618Crossref PubMed Scopus (59) Google Scholar, 3Hübel C. Yilmaz Z. Schaumberg K.E. Breithaupt L. Hunjan A. Horne E. García-González J. O’Reilly P.F. Bulik C.M. Breen G. Body composition in anorexia nervosa: meta-analysis and meta-regression of cross-sectional and longitudinal studies.Int. J. Eat. Disord. 2019; 52: 1205-1223https://doi.org/10.1002/eat.23158Crossref PubMed Scopus (19) Google Scholar Observations from genome-wide association studies (GWASs) suggest that genomic variants that influence body composition are also associated with psychiatric traits.4Watson H.J. Yilmaz Z. Thornton L.M. Hübel C. Coleman J.R.I.I. Gaspar H.A. Bryois J. Hinney A. Leppä V.M. Mattheisen M. et al.Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa.Nat. Genet. 2019; 51: 1207-1214https://doi.org/10.1038/s41588-019-0439-2Crossref PubMed Scopus (293) Google Scholar Genetically, AN is negatively correlated with body mass index (BMI), fat mass, fat-free mass, and obesity,4Watson H.J. Yilmaz Z. Thornton L.M. Hübel C. Coleman J.R.I.I. Gaspar H.A. Bryois J. Hinney A. Leppä V.M. Mattheisen M. et al.Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa.Nat. Genet. 2019; 51: 1207-1214https://doi.org/10.1038/s41588-019-0439-2Crossref PubMed Scopus (293) Google Scholar suggesting that biological mechanisms contributing to AN may also influence body composition. This association is supported by several studies showing that low premorbid BMI is associated with AN in adolescence.5Stice E. Interactive and mediational etiologic models of eating disorder onset: evidence from prospective studies.Annu. Rev. Clin. Psychol. 2016; 12: 359-381https://doi.org/10.1146/annurev-clinpsy-021815-093317Crossref PubMed Scopus (79) Google Scholar,6Tyrka A.R. Waldron I. Graber J.A. Brooks-Gunn J. Prospective predictors of the onset of anorexic and bulimic syndromes.Int. J. Eat. Disord. 2002; 32: 282-290https://doi.org/10.1002/eat.10094Crossref PubMed Scopus (100) Google Scholar Furthermore, an Avon Longitudinal Study of Parents and Children (ALSPAC) study reported that individuals who go on to develop AN followed lower BMI trajectories (as early as age 2 years) compared to their peers that did not develop an eating disorder (ED).7Yilmaz Z. Gottfredson N.C. Zerwas S.C. Bulik C.M. Micali N. Developmental premorbid body mass index trajectories of adolescents with eating disorders in a longitudinal population cohort.J. Am. Acad. Child Adolesc. Psychiatry. 2019; 58: 191-199https://doi.org/10.1016/j.jaac.2018.11.008Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar In contrast to low body weight, high body weight has not only been associated with increased risk for cardiovascular disease but also with psychiatric disorders (e.g., mood disorders and anxiety disorders).8Simon G.E. Von Korff M. Saunders K. Miglioretti D.L. Crane P.K. Van Belle G. Kessler R.C. Association between obesity and psychiatric disorders in the US adult population.Arch. Gen. Psychiatry. 2006; 63: 824-830https://doi.org/10.1001/archpsyc.63.7.824Crossref PubMed Scopus (941) Google Scholar,9González-Muniesa P. Mártinez-González M.-A. Hu F.B. Després J.-P. Matsuzawa Y. Loos R.J.F. Moreno L.A. Bray G.A. Martinez J.A. Obes. Nat. Rev. Dis. Prim. 2017; 3: 17034https://doi.org/10.1038/nrdp.2017.34Crossref PubMed Scopus (526) Google Scholar In addition, individuals with high body weight face stigmatization and discrimination from the public and health professionals, which can exacerbate the negative health effects of obesity.10Tomiyama A.J. Carr D. Granberg E.M. Major B. Robinson E. Sutin A.R. Brewis A. How and why weight stigma drives the obesity “epidemic” and harms health.BMC Med. 2018; 16: 123https://doi.org/10.1186/s12916-018-1116-5Crossref PubMed Scopus (195) Google Scholar, 11Spahlholz J. Baer N. König H.H. Riedel-Heller S.G. Luck-Sikorski C. Obesity and discrimination - a systematic review and meta-analysis of observational studies.Obes. Rev. 2016; 17: 43-55https://doi.org/10.1111/obr.12343Crossref PubMed Scopus (160) Google Scholar, 12Daly M. Sutin A.R. Robinson E. Perceived weight discrimination mediates the prospective association between obesity and physiological dysregulation: evidence from a population-based cohort.Psychol. Sci. 2019; 30: 1030-1039https://doi.org/10.1177/0956797619849440Crossref PubMed Scopus (31) Google Scholar Similar to AN, BMI has been extensively studied on a genetic level and is a heritable polygenic trait.13Locke A.E. Kahali B. Berndt S.I. Justice A.E. Pers T.H. Day F.R. Powell C. Vedantam S. Buchkovich M.L. Yang J. et al.Genetic studies of body mass index yield new insights for obesity biology.Nature. 2015; 518: 197-206https://doi.org/10.1038/nature14177Crossref PubMed Scopus (2468) Google Scholar,14Yengo L. Sidorenko J. Kemper K.E. Zheng Z. Wood A.R. Weedon M.N. Frayling T.M. Hirschhorn J. Yang J. Visscher P.M. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry.Hum. Mol. Genet. 2018; 27: 3641-3649https://doi.org/10.1093/hmg/ddy271Crossref PubMed Scopus (642) Google Scholar Khera et al.15Khera A.V. Chaffin M. Wade K.H. Zahid S. Brancale J. Xia R. Distefano M. Senol-Cosar O. Haas M.E. Bick A. et al.Polygenic prediction of weight and obesity trajectories from birth to adulthood.Cell. 2019; 177: 587-596.e9https://doi.org/10.1016/j.cell.2019.03.028Abstract Full Text Full Text PDF PubMed Scopus (249) Google Scholar reported that a BMI polygenic score (PGS), calculated by summing the BMI-increasing alleles of all variants of a BMI GWAS, weighted by their reported effect sizes,13Locke A.E. Kahali B. Berndt S.I. Justice A.E. Pers T.H. Day F.R. Powell C. Vedantam S. Buchkovich M.L. Yang J. et al.Genetic studies of body mass index yield new insights for obesity biology.Nature. 2015; 518: 197-206https://doi.org/10.1038/nature14177Crossref PubMed Scopus (2468) Google Scholar is associated with body weight at different time points during childhood and adolescence. For example, individuals with a BMI PGS in the top decile have modest yet significantly higher birth weight (+60 g) than individuals with a BMI PGS in the bottom decile.15Khera A.V. Chaffin M. Wade K.H. Zahid S. Brancale J. Xia R. Distefano M. Senol-Cosar O. Haas M.E. Bick A. et al.Polygenic prediction of weight and obesity trajectories from birth to adulthood.Cell. 2019; 177: 587-596.e9https://doi.org/10.1016/j.cell.2019.03.028Abstract Full Text Full Text PDF PubMed Scopus (249) Google Scholar However, this difference in weight increases over time, reaching 3.5 kg by age 8 years and 12.3 kg by age 18 years.15Khera A.V. Chaffin M. Wade K.H. Zahid S. Brancale J. Xia R. Distefano M. Senol-Cosar O. Haas M.E. Bick A. et al.Polygenic prediction of weight and obesity trajectories from birth to adulthood.Cell. 2019; 177: 587-596.e9https://doi.org/10.1016/j.cell.2019.03.028Abstract Full Text Full Text PDF PubMed Scopus (249) Google Scholar These findings highlight differences in growth associated with the polygenic liability to high BMI. In summary, both AN and BMI have a genetic component that can be summarized by PGSs, and these genetic components are inversely correlated. However, it is unclear how genetic risk for both traits, individually and combined, affect growth developmentally during the first two decades of life. We identified individuals considered to be at high risk (defined as PGS in the top two deciles) for either AN or BMI and compared them with their peers with lower risk (defined as PGS in the lower 8 deciles) for the same trait. We then studied the longitudinal effects of the AN PGS4Watson H.J. Yilmaz Z. Thornton L.M. Hübel C. Coleman J.R.I.I. Gaspar H.A. Bryois J. Hinney A. Leppä V.M. Mattheisen M. et al.Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa.Nat. Genet. 2019; 51: 1207-1214https://doi.org/10.1038/s41588-019-0439-2Crossref PubMed Scopus (293) Google Scholar and the BMI PGS14Yengo L. Sidorenko J. Kemper K.E. Zheng Z. Wood A.R. Weedon M.N. Frayling T.M. Hirschhorn J. Yang J. Visscher P.M. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry.Hum. Mol. Genet. 2018; 27: 3641-3649https://doi.org/10.1093/hmg/ddy271Crossref PubMed Scopus (642) Google Scholar (separately and combined) on weight, height, BMI, fat mass index (FMI), lean mass index (LMI), and bone mineral density (BMD) growth trajectories during the first two decades of life using data from ALSPAC.16Fraser A. Macdonald-wallis C. Tilling K. Boyd A. Golding J. Davey smith G. Henderson J. Macleod J. Molloy L. Ness A. et al.Cohort profile: the avon longitudinal study of parents and children: ALSPAC mothers cohort.Int. J. Epidemiol. 2013; 42: 97-110https://doi.org/10.1093/ije/dys066Crossref PubMed Scopus (1165) Google Scholar, 17Boyd A. Golding J. Macleod J. Lawlor D.A. Fraser A. Henderson J. Molloy L. Ness A. Ring S. Davey Smith G. Cohort profile: the ’Children of the 90s’-The index offspring of the avon longitudinal study of parents and children.Int. J. Epidemiol. 2013; 42: 111-127https://doi.org/10.1093/ije/dys064Crossref PubMed Scopus (1578) Google Scholar, 18Golding J. Pembrey M. Jones R. The Alspac Study TeamALSPAC-the avon longitudinal study of parents and children.Paediatr. Perinat. Epidemiol. 2001; 15: 74-87https://doi.org/10.1046/j.1365-3016.2001.00325.xCrossref PubMed Scopus (1077) Google Scholar, 19Golding J. The avon longitudinal study of parents and children (ALSPAC)--study design and collaborative opportunities.Eur. J. Endocrinol. 2004; : U119-U123https://doi.org/10.1530/eje.0.151u119Crossref PubMed Google Scholar, 20Northstone K. Lewcock M. Groom A. Boyd A. Macleod J. Timpson N. Wells N. The Avon Longitudinal Study of Parents and Children (ALSPAC): an update on the enrolled sample of index children in 2019.Wellcome Open Res. 2019; 4: 51https://doi.org/10.12688/wellcomeopenres.15132.1Crossref PubMed Scopus (143) Google Scholar We hypothesized that a higher AN PGS would be associated with slower growth for the weight, BMI, FMI, LMI, and BMD trajectories. Previous studies reported no genetic correlation between height and AN, and therefore we used a height trajectory as a negative control.3Hübel C. Yilmaz Z. Schaumberg K.E. Breithaupt L. Hunjan A. Horne E. García-González J. O’Reilly P.F. Bulik C.M. Breen G. Body composition in anorexia nervosa: meta-analysis and meta-regression of cross-sectional and longitudinal studies.Int. J. Eat. Disord. 2019; 52: 1205-1223https://doi.org/10.1002/eat.23158Crossref PubMed Scopus (19) Google Scholar We also hypothesized that a higher BMI PGS would be associated with faster growth trajectories. Lastly, we hypothesized that individuals with both a high AN PGS and low BMI PGS would represent a subgroup at higher risk for poor growth (slower growth) compared to those with both a low AN PGS and a low BMI PGS. The ALSPAC study is an ongoing population-based birth-cohort study of 14,541 mothers and their children (that were born between April 1, 1991 and December 31, 1992) residing in the southwest of England (UK).16Fraser A. Macdonald-wallis C. Tilling K. Boyd A. Golding J. Davey smith G. Henderson J. Macleod J. Molloy L. Ness A. et al.Cohort profile: the avon longitudinal study of parents and children: ALSPAC mothers cohort.Int. J. Epidemiol. 2013; 42: 97-110https://doi.org/10.1093/ije/dys066Crossref PubMed Scopus (1165) Google Scholar, 17Boyd A. Golding J. Macleod J. Lawlor D.A. Fraser A. Henderson J. Molloy L. Ness A. Ring S. Davey Smith G. Cohort profile: the ’Children of the 90s’-The index offspring of the avon longitudinal study of parents and children.Int. J. Epidemiol. 2013; 42: 111-127https://doi.org/10.1093/ije/dys064Crossref PubMed Scopus (1578) Google Scholar, 18Golding J. Pembrey M. Jones R. The Alspac Study TeamALSPAC-the avon longitudinal study of parents and children.Paediatr. Perinat. Epidemiol. 2001; 15: 74-87https://doi.org/10.1046/j.1365-3016.2001.00325.xCrossref PubMed Scopus (1077) Google Scholar, 19Golding J. The avon longitudinal study of parents and children (ALSPAC)--study design and collaborative opportunities.Eur. J. Endocrinol. 2004; : U119-U123https://doi.org/10.1530/eje.0.151u119Crossref PubMed Google Scholar, 20Northstone K. Lewcock M. Groom A. Boyd A. Macleod J. Timpson N. Wells N. The Avon Longitudinal Study of Parents and Children (ALSPAC): an update on the enrolled sample of index children in 2019.Wellcome Open Res. 2019; 4: 51https://doi.org/10.12688/wellcomeopenres.15132.1Crossref PubMed Scopus (143) Google Scholar From the 15,541 pregnancies, 13,988 were alive at 1 year. At age 7 years, this sample was bolstered with an additional 913 children. The total sample size for analyses using any data collected after the age of 7 is therefore 15,454 pregnancies; of these, 14,901 were alive at 1 year of age. Participants are assessed at regular intervals using clinical interviews, self-report questionnaires, medical records, and physical examinations. Study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at University of Bristol.21Harris P.A. Taylor R. Thielke R. Payne J. Gonzalez N. Conde J.G. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support.J. Biomed. Inform. 2009; 42: 377-381https://doi.org/10.1016/j.jbi.2008.08.010Crossref PubMed Scopus (20557) Google Scholar,22Harris P.A. Taylor R. Minor B.L. Elliott V. Fernandez M. O’Neal L. McLeod L. Delacqua G. Delacqua F. Kirby J. Duda S.N. The REDCap consortium: Building an international community of software platform partners.J. Biomed. Inform. 2019; 95: 103208https://doi.org/10.1016/j.jbi.2019.103208Crossref PubMed Scopus (4244) Google Scholar REDCap is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for data integration and interoperability with external sources. Please note that the study website contains details of all data that are available and includes a fully searchable data dictionary and variable search tool: http://www.bristol.ac.uk/alspac/researchers/our-data/. To avoid potential confounding due to relatedness, one sibling per set of multiple births was randomly selected to guarantee independence of participants; this resulted in the removal of 75 individuals. Furthermore, individuals who are closely related to each other, defined as a phi hat >0.2 (calculated using PLINK v1.90b), were removed; specifically, we removed any duplicates or monozygotic twins, first-degree relatives (parent-offspring and full siblings), and second-degree relatives (half siblings, uncles, aunts, grandparents, and double cousins). The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. The main caregiver initially provided consent for child participation, and from the age 16 years the offspring themselves have provided informed written consent. Numerous measurements of weight and height were collected from different sources (i.e., routine clinic visits, information collected from midwives, linkage to child health records) between birth and age 24 years. Information on weight was collected at research clinic visits annually up to age 14 years and further clinic measurements at ages 16, 18, and 24 years using the Tanita Body Fat Analyzer (Tanita TBFUK Ltd.) to the nearest 50 g. During the same clinic visits, height (standing) was measured to the nearest millimeter with shoes and socks removed using a Holtain stadiometer (Holtain Ltd, Crymych, Pembs, UK). The different measurements of weight and height were highly correlated (Figure S1) across various methods.16Fraser A. Macdonald-wallis C. Tilling K. Boyd A. Golding J. Davey smith G. Henderson J. Macleod J. Molloy L. Ness A. et al.Cohort profile: the avon longitudinal study of parents and children: ALSPAC mothers cohort.Int. J. Epidemiol. 2013; 42: 97-110https://doi.org/10.1093/ije/dys066Crossref PubMed Scopus (1165) Google Scholar,23Micali N. De Stavola B. Ploubidis G. Simonoff E. Treasure J. Field A.E. Adolescent eating disorder behaviours and cognitions: Gender-specific effects of child, maternal and family risk factors.Br. J. Psychiatry. 2015; 207: 320-327https://doi.org/10.1192/bjp.bp.114.152371Crossref PubMed Scopus (68) Google Scholar Information on child and adolescent BMI (weight in kilograms/height squared in meters) was derived using weight and height measurements obtained during clinic visits. All ALSPAC participants were invited to undergo whole-body dual-energy X-ray absorptiometry (DEXA) scans using the Lunar Prodigy DEXA scanner as part of face-to-face visits at the ages of 10, 12, 14, 16, 18, and 24 years. FMI was calculated by dividing total body fat mass (in kilograms) by height (in meters) squared. Similarly, LMI was calculated by dividing total lean mass by height (in meters) squared. Additionally, whole-body (minus head) BMD was also estimated using the Lunar Prodigy DEXA scanner. To derive the trajectories for each outcome, we censored for the presence of any ED, i.e., AN, bulimia nervosa, and binge-eating disorder. Information on a probable ED was available at ages 14, 16, and 18 years (see Micali et al.24Micali N. Solmi F. Horton N.J. Crosby R.D. Eddy K.T. Calzo J.P. Sonneville K.R. Swanson S.A. Field A.E. Adolescent eating disorders predict psychiatric, high-risk behaviors and weight outcomes in young adulthood.J. Am. Acad. Child Adolesc. Psychiatry. 2015; 54: 652-659.e1https://doi.org/10.1016/j.jaac.2015.05.009Abstract Full Text Full Text PDF PubMed Scopus (107) Google Scholar, 25Hübel C. Abdulkadir M. Herle M. Loos R.J.F. Breen G. Bulik C.M. Micali N. One size does not fit all. Genomics differentiates among anorexia nervosa, bulimia nervosa, and binge-eating disorder.Int. J. Eat. Disord. 2021; 54: 785-793https://doi.org/10.1002/eat.23481Crossref PubMed Scopus (19) Google Scholar for more information on how ED diagnoses were derived). The presence of an ED diagnosis at age 14 years meant that all values for that individual regarding their measurement (BMI, FMI, LMI, etc.) at age 14 years up to age 24 years were set to missing. This was also done for the presence of an ED diagnosis at age 16 years (set values at age 16 and beyond to missing) and 18 years (set values at age 18 and beyond to missing). Therefore, censoring did not lead to loss of participants in the analyses, but rather loss of observations (n = 1,055). This censoring allowed us to derive unbiased results in the following longitudinal modeling, while retaining the largest amount of data possible. This is important, as these models are sensitive to outliers and including individuals with EDs would likely introduce extreme values in the distribution. To capture the potential impact of AN on growth, we derived a BMI trajectory across both childhood and adolescence for all participants jointly. Prior to analyses, BMI values (Table S1 and Figure S2) were transformed using the natural log due to the right-skewed distribution of the data. Spline models involve placing spline points (knots) at time points where the direction of growth changes. This is necessary as children’s growth in the first two decades of life is not linear, but follows a more complex pattern, rendering standard growth models unsuitable to accurately reflect the data.26Warrington N.M. Howe L.D. Wu Y.Y. Timpson N.J. Tilling K. Pennell C.E. Newnham J. Davey-Smith G. Palmer L.J. Beilin L.J. et al.Association of a body mass index genetic risk score with growth throughout childhood and adolescence.PLoS One. 2013; 8: e79547https://doi.org/10.1371/journal.pone.0079547Crossref PubMed Scopus (43) Google Scholar The advantage of linear spline models is that they allow knot points to be fitted at different ages to derive periods of change (between the knots) that are approximately linear. After visual inspection of the BMI medians at each time point, two spline points (knots), in addition to the starting point (intercept) at 4 months and the final data wave (24 years), were placed, creating the following periods of linear growth: between ages 4 months and 1 year, between ages 1 and 6.5 years, and between ages 6.5 and 24 years. Mixed-effects spline models were run to describe the longitudinal growth outcomes. The mixed-effects framework lends itself to the analyses of repeated measures as it accounts for the non-independence of measures within an individual. After model fitting, we extracted parameters of slopes using the best linear unbiased predictions (BLUPs). The linear spline modeling resulted in three slopes (coefficients), which correspond with the slopes in the periods of growth between age 4 months and 1 year, growth between age 1 year and 6.5 years, and growth between age 6.5 years and 24 years. Spline models were obtained using STATA (v.15). Genotype data were available for 9,915 out of the total of 15,247 ALSPAC participants. Participants were genome-wide genotyped on the Illumina HumanHap550 quad chip. Following quality control of the genetic data, a total of 8,654 participants with genotyping data and at least one outcome measure were included in the analyses (Table 1). Details of the quality control checks are described in the supplemental information.Table 1Descriptive data from the Avon Longitudinal Study of Parents and Children (ALSPAC)Body composition measureAge (years)FemaleMalenMedian (IQR)nMedian (IQR)BMI (kg/m2)aTo limit the size of this table, only the BMI values at age 10 years and later are shown; for BMI values prior to age 10 years, see Table S1.103,07217.31 (15.77, 19.41)3,02616.77 (15.61, 18.73)122,90218.6 (16.74, 21.14)2,79717.96 (16.43, 20.5)142,43320.17 (18.43, 22.58)2,39419.28 (17.71, 21.45)161,49620.95 (19.47, 23.14)1,28920.83 (19.14, 22.78)181,98622.14 (20.35, 24.78)1,68121.76 (20.08, 24.34)241,67123.63 (21.47, 27.04)1,13124.25 (21.97, 27.09)FMI (kg/m2)102,9324.4 (3.14, 6.18)2,8802.96 (2.08, 4.67)122,8624.91 (3.52, 7.11)2,7503.65 (2.53, 5.9)142,4065.77 (4.28, 7.74)2,3533.11 (2.09, 5.26)161,2666.5 (5.00, 8.36)1,0662.67 (1.83, 4.14)181,8957.13 (5.68, 9.28)1,6243.37 (2.22, 5.64)241,6188.07 (6.43, 10.61)1,1005.7 (4.32, 7.8)LMI (kg/m2)102,93212.07 (11.5, 12.69)2,88012.98 (12.43, 13.55)122,86212.64 (11.95, 13.42)2,75013.28 (12.63, 14)142,40613.39 (12.7, 14.1)2,35314.87 (13.9, 15.92)161,26613.56 (12.81, 14.3)1,06615.93 (14.76, 16.98)181,89513.86 (13.17, 14.64)1,62417.19 (16.16, 18.18)241,61814.8 (13.95, 15.82)1,10017.45 (16.25, 18.83)BMD (g/cm2)102,9650.77 (0.74, 0.81)2,9000.78 (0.75, 0.82)122,8650.85 (0.8, 0.9)2,7560.84 (0.80, 0.89)142,4060.96 (0.91, 1.01)2,3540.95 (0.90, 1.01)162,0501 (0.96, 1.05)1,9521.06 (0.99, 1.12)181,9021.04 (0.99, 1.09)1,6341.14 (1.08, 1.21)241,6191.19 (1.13, 1.26)1,1011.32 (1.23, 1.39)Weight (kg)103,10533.6 (29.6, 38.8)3,04533 (29.4, 37.8)122,90442.8 (37, 50.2)2,80140.6 (35.8, 47.6)142,43353.4 (47.8, 60.2)2,39453 (46.8, 61.35)161,59957 (52, 64)1,33066 (59, 74)181,98661 (55.1, 68.4)1,68370.4 (63.7, 79)241,67164.9 (58.7, 75.55)1,13179 (70.7, 88.5)Height (cm)103,074139.05 (135, 143.4)3,027139.8 (135.7, 143.9)122,904151.4 (146.4, 156.2)2,797149.8 (145.1, 154.7)142,438162.1 (157.8, 166.3)2,394165.2 (158.9, 170.8)161,666165 (160, 170)1,361178 (173, 183)181,988165.1 (161.1, 169.2)1,683179 (174.45, 183.4)241,672165.8 (161.88, 170)1,131180 (175.5, 184.5)Data are presented on age, BMI, FMI, LMI, weight, and height. The sample size per outcome varies. Observations were censored for the presence of an eating disorder (ED), i.e., anorexia nervosa, bulimia nervosa, and binge-eating disorder. Information on a probable ED was available at age 14, 16, and 18 years.24Micali N. Solmi F. Horton N.J. Crosby R.D. Eddy K.T. Calzo J.P. Sonneville K.R. Swanson S.A. Field A.E. Adolescent eating disorders predict psychiatric, high-risk behaviors and weight outcomes in young adulthood.J. Am. Acad. Child Adolesc. Psychiatry. 2015; 54: 652-659.e1https://doi.org/10.1016/j.jaac.2015.05.009Abstract Full Text Full Text PDF PubMed Scopus (107) Google Scholar,25Hübel C. Abdulkadir M. Herle M. Loos R.J.F. Breen G. Bulik C.M. Micali N. One size does not fit all. Genomics differentiates among anorexia nervosa, bulimia nervosa, and binge-eating disorder.Int. J. Eat. Disord. 2021; 54: 785-793https://doi.org/10.1002/eat.23481Crossref PubMed Scopus (19) Google Scholar The presence of an ED diagnosis at age 14 years meant that all values for that individual regarding their measurement (BMI, FMI, LMI, etc.) at age 14 years up to age 24 years were set to missing. This was also done for the presence of an ED diagnosis at age 16 years (set values at age 16 and beyond to missing) and 18 years (set values at age 18 and beyond to missing). BMI was calculated using objectively measured weight and height during a routine clinic visit at age 24 years. Height was measured to the nearest millimeter using a Harpenden Stadiometer (Holtain Ltd.), and weight was measured using the Tanita body fat analyzer (Tanita TBF UK Ltd.) to the nearest 50 grams. FMI, LMI, and BMD were derived using a Lunar Prodigy dual-energy X-ray absorptiometry (DEXA) scanner (GE Medical Systems Lunar, Madison, WI, USA). FMI and LMI were calculated by dividing each measure (in kilograms) by height squared (in meters). BMD was calculated for the whole body excluding the head values. IQR, interquartile range;